Data Science Boot Camp: Week One

My name’s Alexander Chituc, and I’ll be your foreign correspondent in Brussels, regularly reporting on the diHub and the data science community here in Belgium. I’m an American, I studied philosophy at Yale, and I’m one of the seventeen boot-campers for the di-Academy.

It might be an unconventional way to start a Data Science Bootcamp, but the first week was devoted to working on our communication skills with Martine George, PhD, professor of Management Practice at the Solvay Brussels school of Economics and Management. The Director and Head of Marketing Analytics and Research at BNP Paribas Fortis for nearly four years, a database analysis manager for three years, a lecturer on Business Analytics for five, now Martine was teaching us about our personality types and how to effectively communicate with each other, upper management, and potentially, coworkers with drastically different styles of communication.


The main objectives of our training were to make us aware of our own communication style, to learn to adjust the presentations on the results of analytics to different audiences, and how we could convince clients of the importance of our results.

We learned our communication styles through using the Process Communication Model, a tool that “enables you to understand, motivate, and communicate effectively with others.” On the first day, we received our profiles determined by the results of a questionnaire we had taken the week before.

personality-resultsan example of a personality profile

The model divides people into six “base” personalities, with one “phase.” My own “Structure of Personality” had a base of Thinker (organized, responsible, logical), followed by Persister (dedicated, observant, conscientious), Rebel (spontaneous, creative, playful), Imaginer (calm, imaginative, reflective), Promoter (adaptable, persuasive, and charming), and Harmonizer (compassionate, sensitive, warm), in that order (I wont get into too much detail about the different types, except to share the fun fact that in earlier versions of the model, my base personality type, Thinker, was named “Workaholic,” but if you’re interested in learning more, you can visit the website).

six-personality-typesThe second day we focused on communication with managers and giving presentations taken into account what we had learned the first day.

One important aspect of this was writing good one-pager, something a busy executive can quickly read to understand what exactly you’ve learned in your analysis, how you did it, and what to do now. We went over some example one-pagers and explained where they went wrong and how we could improve them: making sure the business question is clear, making the conclusion explicit with an actionable next step, and removing any unnecessary information when explaining the method. No matter how exciting or interesting you might find the methodology of your report, executives and upper management typically don’t.

We also spent a good portion of the second day learning about giving presentations, and how to alter your presentation given a potential change in time. With focusing on governing thoughts, story boarding, and logically organizing our ideas, you can turn a thirty minute presentation into a five minute presentation if the need arises, and vice versa. After some work with Martine, structuring the major key ideas she wanted to express, Annelies gave a great five minute pitch for an app she wanted to build using using data science, and she could just as easily turn it into a thirty minute presentation.

The biggest take away from our training was to target the right group with the right message, and to cater your message not to your own communication style, but the communication style of your audience.

It was an unusual way to start a bootcamp, but communication is an often neglected skill for a data scientists, and beginning this way really put an emphasis on its importance. Next week we would be moving on to Predictive Modeling in R and Data Visualization and Story Telling.

Summer Camps and Leader Boards

My name’s Alexander Chituc, and I’ll be your foreign correspondent in Brussels, regularly reporting on the diHub and the data science community here in Belgium. I’m an American, I studied philosophy at Yale, and I’m one of the seventeen boot-campers for the di-Academy.


Of the hundred or so applicants who applied for the Di Hub’s Data Science Boot Camp, only 40 were selected for the five-week Summer Coding Camp. Most of us had little to no experience coding in Python and R – or in my case, coding – and the Summer Coding Camp was to serve two purposes: first, to narrow down this pool of applicants to the twelve who would eventually be selected for the boot camp, and second, to catch us up as quickly as possible with the coding skills we would need for our training to become data scientists.

I had already expected that there would be a lot to catch up on. I have a bachelor’s degree in philosophy, and my elective coursework was in psychology and writing. My coding experience consisted of one semester in college where I took a class in Object Oriented Programming in Java, seven years ago. Suddenly, I found myself in a room with a couple of Master’s in Statistics, several Master’s in Business Engineering, a few digital marketers, and a lot of data enthusiasts with backgrounds in computer science, all competing for twelve spots.

The Di Hub was open to all of us as a place to study during the camp, providing coaches to answer any questions we might have. Each week of the camp covered a different topic. The first week covered SAS, the second Python, the third R, the fourth statistics, and the fifth SQL. When we began the first week, I was relieved to see just about everybody struggle as much as I did. This didn’t surprise me: all training in SAS comes directly from the company, so regardless of your background, it was no natural that none of us knew how to code in it. It was by far the most intensive week of summer camp, and in the following weeks, many of us were still working on it it, preparing for the certification exam on September 16th, which only half of us passed (I’ll leave it up to you to guess whether I was one of them).

The second, third, and forth weeks we learned using Data Camp’s platform. We were assigned 17 courses to complete on their website: three in Python (at the time, their Python content was admittedly lacking, but they’ve recently added several more Python courses to their website), seven in R, and seven in statistics. During the fourth week, we were given the option of following the courses in R on Data Camp, or to do instead a separate module in SAS. As far as I know, everyone chose to do statistics in R. Doing work in SAS, after all, didn’t count for the leader board.

I should explain the leader board. It began as a joke, Nele announced it that Friday afternoon on Slack. After finishing the day’s coding, we were going to celebrate the completion of our second week, and before this celebration, Nele would be announcing our leader board. Suddenly, all of us became aware of the feature available to groups on Data Camp: a leader board that ranks all of the members of the group by experience earned completing exercises in their courses.

I noticed that I was, at the time, ranked at number twelve, and I was determined to make it into the top ten by the time the day was over. Between exercises, I compulsively checked the status of the leader board, figuring out just how many exercises I had to complete before I would pass number eleven, which I did, and then to pass number 10, which I did. That afternoon, Nele announced the leader board, and on the board were written only six scores. The top six, in descending order were Liza, Goran, Olivier, Agustina, Ruben, Victor, and you can imagine my disappointment.


The leader board was a source of healthy competition, granting bragging rights and a way to measure ourselves against each other and judge our prospects for being selected. It became a little more serious, however, when it was announced that there would be a job fair on September 9th, where we would all present ourselves to companies looking to hire data scientists, and finding a company to sponsor you would guarantee your seat in the boot camp. The order in which we were presenting was determined by your leaderboard ranking.

It was an intense five weeks, and we all learned much more than we could have on our own. I’ll have to devote an entire post to the job fair later on, but I’ll leave off this one by thanking all of the great coaches we had during the summer camp: coaching Python, Elie Jesuran from Keyrus, coaching R, Dominique De Beul, Eric Lecoutre and Pieterjan Geens from Business&Decision, and coaching SQL, Erwin Gurickx from Teradata.